A technique is developed for fusing asynchronous data from two dissimilar sensors, where one sensor provides data at a high rate relative to the other. The idea is to obtain a least-squares estimate of the high data rate sensor data at the time when the other sensor observation is taken. A previously developed synchronous data fusion algorithm is then used to fuse the time aligned data for updating the target state estimates. The case of fusing data from an optical sensor that provides periodic data at a high rate and a radar that provides quasi-periodic data at a low data rate is considered. The performance of a track filter utilizing this data fusion approach is shown via simulation to provide results that are similar to those obtained by the standard sequential data processing approach that requires significantly more computations.